






"""simple docstring"""





import argparse
import os
import re

import packaging.version


_SCREAMING_SNAKE_CASE   :							Tuple          =		'''examples/'''
_SCREAMING_SNAKE_CASE   :							Any          =		{
    '''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
    '''init''': (re.compile(R'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
    '''setup''': (re.compile(R'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), R'''\1version="VERSION",'''),
    '''doc''': (re.compile(R'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
_SCREAMING_SNAKE_CASE   :							Tuple          =		{
    '''init''': '''src/transformers/__init__.py''',
    '''setup''': '''setup.py''',
}
_SCREAMING_SNAKE_CASE   :							Any          =		'''README.md'''







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						List[Any]						,							_lowerCamelCase			:						Any					) ->							Optional[int]:
  with open(_lowerCamelCase						,							'r'						,							encoding='utf-8'						,							newline='\n'					) as f:
    lowerCamelCase_       	=		f.read()
  lowerCamelCase_	,      lowerCamelCase_       	=		REPLACE_PATTERNS[pattern]
  lowerCamelCase_       	=		replace.replace('VERSION'						,							_lowerCamelCase					)
  lowerCamelCase_       	=		re_pattern.sub(_lowerCamelCase						,							_lowerCamelCase					)
  with open(_lowerCamelCase						,							'w'						,							encoding='utf-8'						,							newline='\n'					) as f:
    f.write(_lowerCamelCase					)







def 	lowerCamelCase__			(				_lowerCamelCase			:						Union[str, Any]					) ->							List[str]:
  for folder, directories, fnames in os.walk(_lowerCamelCase					):
    # Removing some of the folders with non-actively maintained examples from the walk
    if "research_projects" in directories:
      directories.remove('research_projects'					)
    if "legacy" in directories:
      directories.remove('legacy'					)
    for fname in fnames:
      if fname.endswith('.py'					):
        update_version_in_file(os.path.join(_lowerCamelCase						,							_lowerCamelCase					)						,							_lowerCamelCase						,							pattern='examples'					)







def 	lowerCamelCase__			(				_lowerCamelCase			:						Any						,							_lowerCamelCase			:						str=False					) ->							Tuple:
  for pattern, fname in REPLACE_FILES.items():
    update_version_in_file(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase					)
  if not patch:
    update_version_in_examples(_lowerCamelCase					)







def 	lowerCamelCase__			(				) ->							Any:
  lowerCamelCase_       	=		'🤗 Transformers currently provides the following architectures'
  lowerCamelCase_       	=		'1. Want to contribute a new model?'
  with open(_lowerCamelCase						,							'r'						,							encoding='utf-8'						,							newline='\n'					) as f:
    lowerCamelCase_       	=		f.readlines()

  # Find the start of the list.
  lowerCamelCase_       	=		0
  while not lines[start_index].startswith(_start_prompt					):
    start_index += 1
  start_index += 1

  lowerCamelCase_       	=		start_index
  # Update the lines in the model list.
  while not lines[index].startswith(_end_prompt					):
    if lines[index].startswith('1.'					):
      lowerCamelCase_       	=		lines[index].replace(
          'https://huggingface.co/docs/transformers/main/model_doc'						,							'https://huggingface.co/docs/transformers/model_doc'						,							)
    index += 1

  with open(_lowerCamelCase						,							'w'						,							encoding='utf-8'						,							newline='\n'					) as f:
    f.writelines(_lowerCamelCase					)







def 	lowerCamelCase__			(				) ->							Tuple:
  with open(REPLACE_FILES['init']						,							'r'					) as f:
    lowerCamelCase_       	=		f.read()
  lowerCamelCase_       	=		REPLACE_PATTERNS['init'][0].search(_lowerCamelCase					).groups()[0]
  return packaging.version.parse(_lowerCamelCase					)







def 	lowerCamelCase__			(				_lowerCamelCase			:						Union[str, Any]=False					) ->							Tuple:
  lowerCamelCase_       	=		get_version()
  if patch and default_version.is_devrelease:
    raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!'					)
  if default_version.is_devrelease:
    lowerCamelCase_       	=		default_version.base_version
  elif patch:
    lowerCamelCase_       	=		F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
  else:
    lowerCamelCase_       	=		F'''{default_version.major}.{default_version.minor + 1}.0'''

  # Now let's ask nicely if that's the right one.
  lowerCamelCase_       	=		input(F'''Which version are you releasing? [{default_version}]'''					)
  if len(_lowerCamelCase					) == 0:
    lowerCamelCase_       	=		default_version

  print(F'''Updating version to {version}.'''					)
  global_version_update(_lowerCamelCase						,							patch=_lowerCamelCase					)
  if not patch:
    print('Cleaning main README, don\'t forget to run `make fix-copies`.'					)
    clean_main_ref_in_model_list()







def 	lowerCamelCase__			(				) ->							Dict:
  lowerCamelCase_       	=		get_version()
  lowerCamelCase_       	=		F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
  lowerCamelCase_       	=		current_version.base_version

  # Check with the user we got that right.
  lowerCamelCase_       	=		input(F'''Which version are we developing now? [{dev_version}]'''					)
  if len(_lowerCamelCase					) == 0:
    lowerCamelCase_       	=		dev_version

  print(F'''Updating version to {version}.'''					)
  global_version_update(_lowerCamelCase					)
  print('Cleaning main README, don\'t forget to run `make fix-copies`.'					)
  clean_main_ref_in_model_list()


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE   :							List[str]          =		argparse.ArgumentParser()
  parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
  parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
  _SCREAMING_SNAKE_CASE   :							List[str]          =		parser.parse_args()
  if not args.post_release:
    pre_release_work(patch=args.patch)
  elif args.patch:
    print('''Nothing to do after a patch :-)''')
  else:
    post_release_work()












"""simple docstring"""





import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


_SCREAMING_SNAKE_CASE   :							int          =		re.compile(R'''\s+''')







def 	lowerCamelCase__			(				_lowerCamelCase			:						Dict					) ->							List[str]:
  return {"hash": hashlib.mda(re.sub(_lowerCamelCase						,							''						,							example['content']					).encode('utf-8'					)					).hexdigest()}







def 	lowerCamelCase__			(				_lowerCamelCase			:						Tuple					) ->							int:
  lowerCamelCase_       	=		[len(_lowerCamelCase					) for line in example['content'].splitlines()]
  return {"line_mean": np.mean(_lowerCamelCase					), "line_max": max(_lowerCamelCase					)}







def 	lowerCamelCase__			(				_lowerCamelCase			:						List[str]					) ->							int:
  lowerCamelCase_       	=		np.mean([c.isalnum() for c in example['content']]					)
  return {"alpha_frac": alpha_frac}







def 	lowerCamelCase__			(				_lowerCamelCase			:						int						,							_lowerCamelCase			:						Optional[int]					) ->							Optional[Any]:
  if example["hash"] in uniques:
    uniques.remove(example['hash']					)
    return True
  else:
    return False







def 	lowerCamelCase__			(				_lowerCamelCase			:						Any						,							_lowerCamelCase			:						Optional[Any]=5					) ->							int:
  lowerCamelCase_       	=		['auto-generated', 'autogenerated', 'automatically generated']
  lowerCamelCase_       	=		example['content'].splitlines()
  for _, line in zip(range(_lowerCamelCase					)						,							_lowerCamelCase					):
    for keyword in keywords:
      if keyword in line.lower():
        return {"autogenerated": True}
  else:
    return {"autogenerated": False}







def 	lowerCamelCase__			(				_lowerCamelCase			:						Tuple						,							_lowerCamelCase			:						Dict=5						,							_lowerCamelCase			:						List[str]=0.05					) ->							Tuple:
  lowerCamelCase_       	=		['unit tests', 'test file', 'configuration file']
  lowerCamelCase_       	=		example['content'].splitlines()
  lowerCamelCase_       	=		0
  lowerCamelCase_       	=		0
  # first test
  for _, line in zip(range(_lowerCamelCase					)						,							_lowerCamelCase					):
    for keyword in keywords:
      if keyword in line.lower():
        return {"config_or_test": True}
    # second test
  lowerCamelCase_       	=		example['content'].count('\n'					)
  lowerCamelCase_       	=		int(coeff * nlines					)
  for line in lines:
    count_config += line.lower().count('config'					)
    count_test += line.lower().count('test'					)
    if count_config > threshold or count_test > threshold:
      return {"config_or_test": True}
  return {"config_or_test": False}







def 	lowerCamelCase__			(				_lowerCamelCase			:						Any					) ->							List[str]:
  lowerCamelCase_       	=		['def ', 'class ', 'for ', 'while ']
  lowerCamelCase_       	=		example['content'].splitlines()
  for line in lines:
    for keyword in keywords:
      if keyword in line.lower():
        return {"has_no_keywords": False}
  return {"has_no_keywords": True}







def 	lowerCamelCase__			(				_lowerCamelCase			:						Dict						,							_lowerCamelCase			:						Dict=4					) ->							Optional[Any]:
  lowerCamelCase_       	=		example['content'].splitlines()
  lowerCamelCase_       	=		0
  for line in lines:
    counter += line.lower().count('='					)
    if counter > minimum:
      return {"has_few_assignments": False}
  return {"has_few_assignments": True}







def 	lowerCamelCase__			(				_lowerCamelCase			:						Dict					) ->							List[str]:
  lowerCamelCase_       	=		tokenizer(example['content']						,							truncation=_lowerCamelCase					)['input_ids']
  lowerCamelCase_       	=		len(example['content']					) / len(_lowerCamelCase					)
  return {"ratio": ratio}







def 	lowerCamelCase__			(				_lowerCamelCase			:						Optional[Any]					) ->							List[Any]:
  lowerCamelCase_       	=		{}
  results.update(get_hash(_lowerCamelCase					)					)
  results.update(line_stats(_lowerCamelCase					)					)
  results.update(alpha_stats(_lowerCamelCase					)					)
  results.update(char_token_ratio(_lowerCamelCase					)					)
  results.update(is_autogenerated(_lowerCamelCase					)					)
  results.update(is_config_or_test(_lowerCamelCase					)					)
  results.update(has_no_keywords(_lowerCamelCase					)					)
  results.update(has_few_assignments(_lowerCamelCase					)					)
  return results







def 	lowerCamelCase__			(				_lowerCamelCase			:						Any						,							_lowerCamelCase			:						Dict						,							_lowerCamelCase			:						Optional[Any]					) ->							Any:
  if not check_uniques(_lowerCamelCase						,							_lowerCamelCase					):
    return False
  elif example["autogenerated"]:
    return False
  elif example["line_max"] > args.line_max:
    return False
  elif example["line_mean"] > args.line_mean:
    return False
  elif example["alpha_frac"] < args.alpha_frac:
    return False
  elif example["ratio"] < args.min_token_ratio:
    return False
  elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
    return False
  elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
    return False
  elif example["has_few_assignments"]:
    return False
  else:
    return True







def 	lowerCamelCase__			(				_lowerCamelCase			:						str					) ->							int:
  with open(_lowerCamelCase						,							'rb'					) as f_in:
    with gzip.open(str(_lowerCamelCase					) + '.gz'						,							'wb'						,							compresslevel=6					) as f_out:
      shutil.copyfileobj(_lowerCamelCase						,							_lowerCamelCase					)
  os.unlink(_lowerCamelCase					)


# Settings
_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		HfArgumentParser(PreprocessingArguments)
_SCREAMING_SNAKE_CASE   :							Any          =		parser.parse_args()
if args.num_workers is None:
  _SCREAMING_SNAKE_CASE   :							List[str]          =		multiprocessing.cpu_count()
_SCREAMING_SNAKE_CASE   :							str          =		AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		time.time()
_SCREAMING_SNAKE_CASE   :							int          =		load_dataset(args.dataset_name, split='''train''')
print(F'''Time to load dataset: {time.time()-t_start:.2f}''')

# Run preprocessing
_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		time.time()
_SCREAMING_SNAKE_CASE   :							Tuple          =		ds.map(preprocess, num_proc=args.num_workers)
print(F'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

# Deduplicate hashes
_SCREAMING_SNAKE_CASE   :							List[Any]          =		set(ds.unique('''hash'''))
_SCREAMING_SNAKE_CASE   :							Optional[int]          =		len(uniques) / len(ds)
print(F'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
_SCREAMING_SNAKE_CASE   :							Dict          =		time.time()
_SCREAMING_SNAKE_CASE   :							Tuple          =		ds.filter(filter, fn_kwargs={'''uniques''': uniques, '''args''': args})
print(F'''Time to filter dataset: {time.time()-t_start:.2f}''')
print(F'''Size of filtered dataset: {len(ds_filter)}''')

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
  _SCREAMING_SNAKE_CASE   :							Optional[Any]          =		time.time()
  _SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		deduplicate_dataset(ds_filter, args.jaccard_threshold)
  print(F'''Time to deduplicate dataset: {time.time()-t_start:.2f}''')
  print(F'''Size of deduplicate dataset: {len(ds_filter)}''')

# Save data in batches of samples_per_file
_SCREAMING_SNAKE_CASE   :							Dict          =		Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
  with open(output_dir / '''duplicate_clusters.json''', '''w''') as f:
    json.dump(duplicate_clusters, f)

_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
  _SCREAMING_SNAKE_CASE   :							Dict          =		str(data_dir / F'''file-{file_number+1:012}.json''')
  _SCREAMING_SNAKE_CASE   :							Any          =		min(len(ds_filter), index + args.samples_per_file)
  ds_filter.select(list(range(index, end_index))).to_json(file_path)
  compress_file(file_path)






print(F'''Time to save dataset: {time.time()-t_start:.2f}''')




